How to use Big Data Analytics as a Competitive Advantage

December 16, 2015
Blaine Mathieu's picture
Chief Marketing Officer

Business Intelligence (BI) is crucial to companies and especially, to executives.

In fact, BI enables managers to make smarter decisions by providing them with a broad and accurate picture of the business.

However, BI needs big data and analytics in order to accomplish its mission. Nowadays, the process of applying analytics to big data is found in BI platforms.  

Today, companies are looking to their BI platforms to drive competitive differentiation.

Whether through generating revenue opportunities or providing opportunities for unique analytics offerings, BI platforms are themselves a component of a forward-thinking business’ data strategy.

Cloudswave sat down with  leading BI platform, GoodData’s Chief Marketing and Product Officer, Blaine Mathieu, to clarify the mystery of BI and analytics.

GoodData enables its customers to unlock the value of their data through a scalable analytics distribution platform. It provides its clients with automated and powerful BI tools that improve their relationships with their partners and customers.  

Interview with Blaine Mathieu, Chief Marketing and Product Officer at GoodData:

1. What does 2016 have in store for business intelligence (BI)?

2016 will be an extraordinary time in the BI space because it will be all aboutexpanding the impact of BI beyond the data analysts and scientists into the realm of real business decision makers and practitioners.

The promise of BI has traditionally been about empowering businesses to makebetter decisions, but the reality has been different.

Data is locked inside organizations and limited to small internal teams of marketers, sales leaders, or execs, and is usually managed and controlled by analysts or – more recently – data scientists.

For that reason, BI has not yet lived up to its potential.

In 2016 that will change as more companies unlock the value of data and distribute it to their business networks, including their clients, partners, suppliers, business units, and other distributed internal and external organizations where real business decisions are made every day.

2. How would you rate the importance of business intelligence software to small and medium businesses?

BI is not as important to SMBs as it is to larger enterprises. What is important is having access to data with engaging analytics so that real business decisions can be made.

Many SMBs are part of the ‘business network’ of a larger organization.

A good example is the Firehouse Sub franchise, which is made of hundreds of franchisors who are SMBs. These SMBs are now using analytics to make better decisions and improve their business prospects every single day, while not maintaining their own time and resource intensive BI system.

3. Can they survive using the traditional BI; in other words, without using a BI software?

It’s not a question of survival.

It’s about creating efficiency, driving growth, and being more successful from customer experience and revenue perspectives!

4. What is the relationship between Big Data, Business Intelligence and business analytics? What is the difference?

At the end of the day, it’s not the buzzwords that matter.

What matters is unlocking the value of data by distributing it to your business networks and partners with engaging one-to-many analytics. Is that BI? Yes. BA? Yes. Could it involve so-called big-data? Sure!

5. How can companies leverage the value of their Big Data investments in business intelligence?

Big data just means that enterprises are collecting increasingly huge volumes of data, often driven by the multitude of data sources, sensors and devices that we directly engage with and that surround us today.

I believe the best way to leverage the value of big data is to not leave it in the hands of data scientists and analysts, but to transform it into engaging analytics and distribute those to more  business people who need to make better, faster decisions every day.

6. What challenges can they expect to face during this process?

BI tools have historically been very complex – basically Excel on steroids.

To simplify this, pre-canned reports are created that are usually out of date by the time they are used. Then it’s back to the IT department to create new reports and the cycle continues.

Distributing analytics to a network of business partners, clients, or business units is extremely useful for those involved, but the technologies are quite complex. Wrapping a layer of intelligence around the data is critical so that the complexity of the underlying data is hidden from the business user.

Setting up the systems to enable massive distribution is non-trivial. Automatically managing all these organizations and users, at large scale, is a huge undertaking.

The good news is that solutions exist to make this all incredibly easy so that organizations don’t need to become experts in BI or analytics distribution. They just need to have the data – and that is something most companies have lots of.

7. In one of your blog articles, it is mentioned that “advanced analytics of big data is intimately tied to big money opportunities for companies moving forward”. Can you tell us more about this point?

We sometimes call that Enterprise Data Monetization.

This does not mean that companies are ‘selling’ their data. Rather, it means companies can unlock the value of their data by distributing it to their internal or external business networks.

Once in place, this immediately creates value by increasing client retention, improving partner relationships, and ultimately enabling new revenue streams or enhancing existing ones.

There is definitely a large opportunity to make money and reduce costs when companies can unlock the value of their data for themselves and for their networks.

8. What are the best practices to help bring data products to market?

Great question.

So far, we have been talking about the technology aspect of analytics distribution. But, just as important, is the expertise that companies require in order to conceptualize, create, and bring to market data-oriented products for their business networks.

Few companies have this expertise internally and they need to look to experts to help them to make critical enhancements to their business models.

9. A year ago you released “The Ultimate Guide to Embedded Analytics,” in which it states that “GoodData found that the biggest challenge of customer-facing analytics was not related to technology, but rather to developing effective go-to-market strategies”.

Can you define what a go-to-market strategy is for embedded analytics and give us examples of effective ones? Why is developing effective go-to-market strategies the biggest challenge of customer-facing analytics?

We find that it is best to begin with a dedicated workshop to bring key stakeholders together and begin to conceptualize what real value of the data could be if it could be distributed to those who could make best use of it.

From there, a broad strategy is conceptualized, created and developed to launch the particular data ‘product.’ This may or may not include a pricing model, if the combination of data and analytics is actually to be sold as a revenue-generating offering.

Finally, there may be training involved – although usually this is for the administrators of the system since it is so easy to use and learn for the end business users. After that, it’s on to launch.t Then, learning and optimization.

Without an effective go-to-market strategy, the danger is that the value of the data won’t be unlocked.

A great example of a company actually  leveraging this value is ServiceChannel, a facilities management software company that partnered with GoodData.

Over the years, it has brought together a tremendous amount of data on facilities management metrics and best practices that is now being distributed (in this case, sold as a service) to large corporations around the world. Simultaneously, they are using this data to optimize the management of their own facilities.

10. Tell us a bit about the GoodData story? How did the company get started? What makes GoodData stand apart from the numerous other BI and Analytics vendors?

GoodData started about seven years ago with the vision of unlocking the value of engaging analytics beyond the data scientists and analysts.

Over time, the realization developed that the best way to do this was to enable the distribution of data, wrapped in analytics, beyond the small internal teams that have historically utilized BI tools.

Since then, GoodData has been perfecting its analytics distribution platform technology and has also been developing the expertise to help clients, such as ISVs and enterprises to actually create and launch their desired initiatives.

Reposted with permission from Cloudswave.

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